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Paper Citation Record · LEDGER

Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering

As of 17 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 2 inbound Pith citation observations for arXiv:2506.11021.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.11021 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:56:36.046570Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-03T09:01:48.501745Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

27 of 27 outbound references displayed

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External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation adcb272a-9a35-4fa4-be25-95c344662058 · outbound

This paper cites The claude 3 model family: Opus, sonnet, haiku, March 2024.

Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering The claude 3 model family: Opus, sonnet, haiku, March 2024

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 6b4bf681-33d7-42d8-b2e8-46a1d89dd279 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering Evaluating Large Language Models Trained on Code

Reference 2

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Observation 8dfbf4b4-f180-428f-af56-1b460d69d2f1 · outbound

This paper cites Calibration of pre-trained transformers.

Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering Calibration of pre-trained transformers

Reference 3

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Observation d83f8731-8d89-40f4-bb5d-1bf2aed3d27c · outbound

This paper cites Detecting hallucinations in large language models using semantic entropy.

Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering Detecting hallucinations in large language models using semantic entropy

Reference 4

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Unavailable: canonical work link unavailable.

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Observation bc338a2c-93e0-464e-b7a3-db07acce918c · outbound

This paper cites A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions.

Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions

Reference 5

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Observation 0936f32b-d05f-41a4-a627-1ec40b352c56 · outbound

This paper cites LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code.

Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Reference 6

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Source-reported events for the cited work

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Observation f7635375-d102-44ba-b47d-3e8e00838381 · outbound

This paper cites Language Models (Mostly) Know What They Know.

Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering Language Models (Mostly) Know What They Know

Reference 7

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Observation 77ac6d32-49ca-4561-8b9d-c2d97454d86a · outbound

This paper cites Semantic uncertainty: Linguistic invari- ances for uncertainty estimation in natural language generation.

Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering Semantic uncertainty: Linguistic invari- ances for uncertainty estimation in natural language generation

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 436d1e5a-35e0-4f2f-a0a4-3eebe71c390a · outbound

This paper cites Teaching models to express their uncertainty in words.

Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering Teaching models to express their uncertainty in words

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation b8cf6b47-d3d1-4bc4-9afb-4c16211120c2 · outbound

This paper cites Exploring and evaluating hallucinations in llm-powered code generation,.

Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering Exploring and evaluating hallucinations in llm-powered code generation,

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation d9b85b58-0a03-4315-8e7c-08eeff93f1c4 · outbound

This paper cites Litcab: Lightweight language model calibration over short and long-form responses.

Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering Litcab: Lightweight language model calibration over short and long-form responses

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation b340f3ae-d3e4-40ff-b65b-93201a4cd1b6 · outbound

This paper cites Estimating LLM Uncertainty with Evidence.

Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering Estimating LLM Uncertainty with Evidence

Reference 12

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Observation 368616d3-32c0-4e08-89e9-647127ea4802 · outbound

This paper cites Introducing gpt-4.1 in the api, April 2025.

Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering Introducing gpt-4.1 in the api, April 2025

Reference 13

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Observation c186334f-24f3-41d9-9005-30873d100299 · outbound

This paper cites GPT-4o System Card.

Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering GPT-4o System Card

Reference 14

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Observation f1cc0fa0-e105-4927-ab05-64f70375c72a · outbound

This paper cites Competitive Programming with Large Reasoning Models.

Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering Competitive Programming with Large Reasoning Models

Reference 15

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Observation f5aecc70-65a4-416c-8b99-7234ef6a4854 · outbound

This paper cites Semantic Density: Uncertainty Quantification for Large Language Models through Confidence Measurement in Semantic Space.

Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering Semantic Density: Uncertainty Quantification for Large Language Models through Confidence Measurement in Semantic Space

Reference 16

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Observation 5751acfa-5cc0-4769-b2a1-7139f6c68bb4 · outbound

This paper cites Assessing Correctness in LLM-Based Code Generation via Uncertainty Estimation.

Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering Assessing Correctness in LLM-Based Code Generation via Uncertainty Estimation

Reference 17

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Observation b9da7232-80ca-412e-a5da-b6aeaa96e25d · outbound

This paper cites Magis: Llm-based multi-agent framework for github issue resolution.

Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering Magis: Llm-based multi-agent framework for github issue resolution

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 8b627423-8e32-4eb4-bdc4-f41efd45c907 · outbound

This paper cites an unresolved cited work.

Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering Unresolved cited work

Reference 19

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Observation a110b054-5ca4-4f96-9d6f-e4e7039785e8 · outbound

This paper cites CodeHalu: Investigating Code Hallucinations in LLMs via Execution-based Verification.

Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering CodeHalu: Investigating Code Hallucinations in LLMs via Execution-based Verification

Reference 20

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Observation 6a2dcf32-4820-4ce5-a664-1e197ee64ed3 · outbound

This paper cites A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models.

Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models

Reference 21

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Observation b15242db-5c6e-41e1-b5cb-d49d17bae170 · outbound

This paper cites Llm perfor- mance assessment in computer science graduate entrance exams.

Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering Llm perfor- mance assessment in computer science graduate entrance exams

Reference 22

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Observation cc91b9b8-7e47-407d-955b-2d201c7cdda9 · outbound

This paper cites Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs.

Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs

Reference 23

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Observation af0f3b83-99a5-4799-8bd5-1c05523a718e · outbound

This paper cites Mitigating LLM Hallucinations via Conformal Abstention.

Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering Mitigating LLM Hallucinations via Conformal Abstention

Reference 24

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Observation cacc16af-496c-4296-8c9f-7faa68f42645 · outbound

This paper cites Benchmarking LLMs via Uncertainty Quantification.

Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering Benchmarking LLMs via Uncertainty Quantification

Reference 25

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Observation 4b82cfb8-3dc3-4625-8a40-2b5877b1b911 · outbound

This paper cites A Survey of Large Language Models.

Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering A Survey of Large Language Models

Reference 26

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source=pdf_text observed=2026-08-15T20:56:36.046570Z digest=sha256:4db3caafeb66cf9565464368cc8b2279393e655cccaf8a621600de7744984d91

Observation dc1f39d6-07f1-4763-82ec-bd93c09a9328 · outbound

This paper cites an unresolved cited work.

Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering Unresolved cited work

Reference 2024

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source=pdf_text observed=2026-08-15T20:56:35.990105Z digest=sha256:b98299e9dbd3e5231ad20b06d3748b56f8ead5e396a088421de88adf027799b8

Pith citing papers

Observation cbc18774-863a-4c44-868b-10128397a22d · inbound

Ensemble-Based Uncertainty Estimation for Code Correctness Estimation cites this paper.

Ensemble-Based Uncertainty Estimation for Code Correctness Estimation Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering

Reference 35

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arxiv_id, observed 2026-05-14T22:53:14.101816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 11f700b5-386e-4373-8d2e-cb35008784d0 · inbound

Underspecification does not imply Incoherence: The Risks of Semantic Collapse in Coding Models cites this paper.

Underspecification does not imply Incoherence: The Risks of Semantic Collapse in Coding Models Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering

Reference 23

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arxiv_id, observed 2026-07-03T09:07:46.997095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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